[R-sig-ME] lmer versus glm results

[Yuqing Ren]

Excellent! This is exactly the answer I was looking for and it makes
perfect sense.

Thank you, John and Tom for your help.

Ching

On Thu, May 26, 2011 at 3:03 AM, John Maindonald
<john.maindonald at anu.edu.au> wrote:
> The more relevant comparison is between
> 1) {
> glm(leaving ~ quarter + project_scope + project_size + tenure + pastwork
> + <additional fixed effect terms that account, now as fixed effectsm for the same
> main effects and interactions as ( 1 + quarter | project_id) + (1 + quarter | user_id)>,
> family=binomial("logit"), data=all)
>
> [replacing the part between the diamond brackets (< >) by something that R can
> interpret is left as an exercise for anyone who might welcome such a challenge!]
> }
>
> and 2) {
> lmer(leaving ~ quarter + project_scope + project_size + tenure +
> pastwork + ( 1 + quarter | project_id) + (1 + quarter | user_id),
> family=binomial, data=all)
> }
>
> Note that the coefficient estimates are conditional on other effects for which the
> relevant equation accounts.  Change those other effects and you are likely to
> change the coefficients, and the coefficient estimates.
>
> What is probably a second order effect (& not needed to explain what you see
> here) is that the relative weighting of the observations will be different in the
> random effects analysis, even for a 'relevant' comparison.
>
> The following makes the point re interpretation of regression coefficients well,
> albeit in a standard least squares regression context:
> "Interpreting Regression Coefficients", at:
> http://www.mosaic-web.org/MCAST/videos/MCAST-2010-09-10/lib/playback.html
>
> This is one in a series of "M-casts".  A complete list is at:
> http://www.causeweb.org/wiki/mosaic/index.php/Pub100
>
> John Maindonald             email: john.maindonald at anu.edu.au
> phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
> Centre for Mathematics & Its Applications, Room 1194,
> John Dedman Mathematical Sciences Building (Building 27)
> Australian National University, Canberra ACT 0200.
> http://www.maths.anu.edu.au/~johnm
>
> On 26/05/2011, at 2:53 PM, Yuqing Ren wrote:
>
>> Dear Tom,
>>
>> Thanks very much for your response. Here are the commands I ran.
>>
>> glm(leaving ~ quarter + project_scope + project_size + tenure +
>> pastwork, family=binomial("logit"), data=all)
>> lmer(leaving ~ quarter + project_scope + project_size + tenure +
>> pastwork + ( 1 + quarter | project_id) + (1 + quarter | user_id),
>> family=binomial, data=all)
>>
>> Ching
>>
>> On Wed, May 25, 2011 at 3:38 PM, Thomas Levine <tkl22 at cornell.edu> wrote:
>>> Could you post the commands you ran?
>>>
>>> Tom
>>>
>>> On Wed, May 25, 2011 at 12:25 PM, Yuqing Ren <chingren at umn.edu> wrote:
>>>>
>>>> Dear All,
>>>>
>>>> I have a quick questions about comparing results from lmer and from
>>>> glm. We are running analysis to predict a person's likelihood of
>>>> leaving a project with some people affiliated with multiple projects
>>>> (binary outcome and crossed random effects).
>>>>
>>>> The data consist of three levels: projects, members (crossed with
>>>> projects with 70% members with one project and 30% with multiple
>>>> projects), and time series nested within individuals. I ran the
>>>> analysis with first glm (family=binomial) and then lmer
>>>> (family-binomial and + (1 | projectid) + (1 | memberid) to account for
>>>> the random effects). The two analyses have the same covariates:
>>>> project size and scope and some individual member attributes such as
>>>> tenure and past performance.
>>>>
>>>> Theoretically, I expect the coefficients to be similar between the two
>>>> results with some differences in the significance test or confidence
>>>> intervals. However, I found three coefficients flipped signs between
>>>> the two, which is very puzzling. I ran another set of analysis with a
>>>> continuous dependent variable (quantity of work completed) and found
>>>> similar coefficients between the two (results from lm and lmer).
>>>>
>>>> So my question is: should we expect the results from glm and lmer to
>>>> be similar? If we should see different results, is it because of the
>>>> distribution being binomial rather than normal or other reasons? Which
>>>> set of results is more reliable and should be included in our paper?
>>>>
>>>> Thanks very much.
>>>>
>>>> Ching Ren
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models at r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>>
>>
>>
>>
>> --
>> Yuqing (Ching) Ren
>> Assistant Professor at Carlson School of Management
>> University of Minnesota, CSOM 3-370
>> 321 19th Avenue S., Minneapolis, MN 55455
>> (tel) 612-625-5242 (fax) 612-626-1316
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>



-- 
Yuqing (Ching) Ren
Assistant Professor at Carlson School of Management
University of Minnesota, CSOM 3-370
321 19th Avenue S., Minneapolis, MN 55455
(tel) 612-625-5242 (fax) 612-626-1316